Optimization of AI-Based Automatic Timetable Scheduling in Gombe State Polytechnic, Gombe State
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- NGN 5000
Background of the Study
Timetable scheduling in educational institutions is a complex task that involves assigning courses to available classrooms, managing faculty schedules, and ensuring that students can take the courses they need without conflicts. Traditionally, timetable creation has been a manual, time-consuming process that requires careful coordination between departments, faculty, and students. However, with the rise of Artificial Intelligence (AI), institutions now have the opportunity to automate and optimize this process, ensuring better resource utilization and minimizing scheduling conflicts.
At Gombe State Polytechnic, Gombe State, the scheduling of classes is often inefficient, leading to issues such as classroom shortages, time conflicts, and administrative overload. AI-based systems can analyze past scheduling data, predict course demand, and optimize the allocation of classrooms and teaching staff. This study will explore the design and implementation of an AI-based automatic timetable scheduling system for Gombe State Polytechnic, focusing on its potential to improve efficiency and reduce scheduling conflicts.
Statement of the Problem
The current manual timetable scheduling process at Gombe State Polytechnic is prone to errors and inefficiencies, leading to scheduling conflicts, overburdened classrooms, and difficulties in accommodating both student and faculty preferences. Despite the growing complexity of scheduling requirements, there is no AI-based solution in place to optimize this process. This study aims to develop an AI-based automatic timetable scheduling system that will help automate the scheduling process and reduce inefficiencies.
Objectives of the Study
1. To design and implement an AI-based automatic timetable scheduling system for Gombe State Polytechnic.
2. To evaluate the effectiveness of the AI-based system in reducing scheduling conflicts and optimizing resource utilization.
3. To assess the impact of the AI-based timetable scheduling system on administrative workload and efficiency.
Research Questions
1. How effective is the AI-based automatic timetable scheduling system in reducing scheduling conflicts at Gombe State Polytechnic?
2. What impact does the AI-based system have on the efficient utilization of classrooms and faculty resources?
3. How does the AI-based system affect the administrative workload and efficiency at Gombe State Polytechnic?
Research Hypotheses
1. The AI-based automatic timetable scheduling system reduces scheduling conflicts compared to the manual scheduling method.
2. The AI-based system optimizes the utilization of classrooms and teaching staff at Gombe State Polytechnic.
3. The implementation of the AI-based timetable scheduling system decreases the administrative workload associated with scheduling.
Significance of the Study
This study will help Gombe State Polytechnic improve its scheduling system by implementing an AI-based solution that reduces conflicts and improves resource allocation. The research will contribute to the growing body of knowledge on AI applications in educational administration and could serve as a model for other institutions facing similar scheduling challenges.
Scope and Limitations of the Study
The study will focus on Gombe State Polytechnic and the design and implementation of an AI-based timetable scheduling system. Limitations include potential resistance to adopting the system, technological infrastructure constraints, and the complexity of integrating the system with existing administrative processes.
Definitions of Terms
• AI-Based Timetable Scheduling System: A system powered by artificial intelligence that automates the creation and optimization of class schedules, taking into account available resources and minimizing conflicts.
• Scheduling Conflicts: Situations where classes or exams overlap, or resources such as classrooms or faculty are double-booked.
• Resource Utilization: The efficient use of available classrooms, teaching staff, and other resources in the scheduling process.
• Administrative Workload: The amount of work involved in planning, coordinating, and managing institutional processes such as class scheduling.
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